CN112862289A - Information matching method and device for clinical research practitioner - Google Patents
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Abstract
The invention relates to an information matching method and device for clinical research practitioners, wherein the method comprises the following steps: acquiring basic information and behavior information of clinical research practitioners in each business system; constructing a label library based on the basic information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a group of labels; receiving a partner request for a clinical study item, the partner request for requesting a match of a clinical study practitioner for the clinical study item; and outputting information of the target clinical research practitioner in response to the partner request, wherein a set of labels corresponding to the target clinical research practitioner is matched with the clinical research project. By the method and the device, the problems of non-uniform management and low management efficiency and accuracy of user information of clinical research practitioners are solved, the cost of maintaining the user information of the clinical research practitioners by a system is saved, and the clinical research project partners can be more accurate and efficient by constructing the user label library of the clinical research practitioners.
Description
Technical Field
The invention relates to the technical field of clinical research analysis, in particular to an information matching method, an information matching device, computer equipment and a computer readable storage medium for clinical research practitioners.
Background
At present, the user information of clinical research practitioners is not uniformly managed, and the management efficiency and accuracy are low, which are specifically expressed in the following aspects:
1. the user information of clinical research practitioners of each business system is maintained by each business system, the user information of the same entity is dispersed, the user behaviors of all business systems cannot establish relevance, the user information of the same entity is single and incomplete, users cannot be comprehensively known and analyzed, and the management and maintenance of the user information waste resources.
2. The logs generated by the user in the service system cannot be effectively collected, the movement of a practitioner in the service system cannot be effectively and comprehensively mastered, the operation partner cannot predict the movement of the user in advance, and the project partner has certain blindness, so that the execution efficiency of clinical research projects is influenced.
3. The user data is marked manually, so that the efficiency is low, the cost is high, certain marking errors exist, and the marked information is difficult to update and maintain.
At present, no effective solution is provided for the problems of non-uniform management of user information and low management efficiency and accuracy of clinical research practitioners in the related art.
Disclosure of Invention
The present application aims to overcome the defects in the prior art, and provides an information matching method, an apparatus, a computer device and a computer-readable storage medium for clinical research practitioners, so as to solve at least the problems of non-uniform management and low management efficiency and accuracy of user information of clinical research practitioners in the related art.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides an information matching method for clinical research practitioners, including:
acquiring basic information of clinical research practitioners in each business system;
acquiring behavior information of the clinical research practitioner in each business system;
constructing a label library based on the base information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels;
receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item;
outputting information of a target clinical research practitioner in response to the partner request, wherein a set of labels corresponding to the target clinical research practitioner matches the clinical research project.
In some embodiments, constructing a tag library based on the base information and the behavior information comprises:
extracting a fact label corresponding to the clinical research practitioner from the basic information, wherein the fact label is used for indicating at least one of the following information of the clinical research practitioner: social attributes, clinical study attributes, project performance, content of interest, use tools, liveness;
generating a model label corresponding to the clinical research practitioner according to the fact label and the behavior information, wherein the model label is used for indicating at least one of the following information of the clinical research practitioner: the city where the user is located, the facultative intention of the user, the user score and the project experience distribution;
generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label, wherein the predictive label is indicative of at least one of the following information for the clinical research practitioner: demographic attributes, content of interest, recent trends, potential for user participation in clinical research projects;
wherein the set of tags includes at least one of the fact tag, the model tag, and the prediction tag.
In some of these embodiments, generating a model label for the clinical research practitioner from the fact label and the behavioral information comprises:
and taking the fact label and the behavior information as input parameters of a pre-trained model, obtaining output parameters of the pre-trained model, and determining the output parameters as the model labels corresponding to the clinical research practitioners, wherein the pre-trained model is used for indicating the corresponding relationship between the fact label corresponding to the clinical research practitioners and the behavior information of the clinical research practitioners and the model labels corresponding to the clinical research practitioners.
In some of these embodiments, generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label comprises:
clustering the clinical research practitioners according to the fact labels and the model labels;
the predictive label is configured for each type of clinical research practitioner.
In some embodiments, after building the tag library based on the base information and the behavior information, the method further comprises:
pushing information to the clinical research practitioner that matches a set of labels corresponding to the clinical research practitioner.
In some of these embodiments, after obtaining information on the behaviour of the clinical research practitioner in the respective business system, the method further comprises:
normalizing the behavior information to obtain processed behavior information, wherein the processed behavior information comprises: a time stamp indicating a time at which an event was performed by the clinical research practitioner, an event identification identifying an event performed by the clinical research practitioner, and a behavior identification indicating a behavior action made during the performance of an event by the clinical research practitioner;
and monitoring the user behavior of the clinical research practitioner according to the processed behavior information.
In some of these embodiments, monitoring the user behaviour of the clinical research practitioner from the processed behaviour information comprises:
user behavior of the clinical research practitioner is monitored from at least one of a time dimension, an event dimension, and a behavior dimension.
In a second aspect, an embodiment of the present application provides an information matching apparatus for a clinical research practitioner, including:
the first acquisition unit is used for acquiring basic information of clinical research practitioners in each business system;
the second acquisition unit is used for acquiring behavior information of the clinical research practitioner in each business system;
a construction unit configured to construct a label library based on the basic information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels;
a receiving unit for receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item;
and the output unit is used for responding to the partner request and outputting the information of the target clinical research practitioner, wherein a group of labels corresponding to the target clinical research practitioner is matched with the clinical research project.
In a third aspect, the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the information matching method for a clinical research practitioner as described in the first aspect above when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium on which is stored a computer program which, when executed by a processor, implements the information matching method for clinical research practitioners as described in the first aspect above.
By adopting the technical scheme, compared with the prior art, the information matching method for the clinical research practitioner provided by the embodiment of the application acquires the basic information of the clinical research practitioner in each business system; acquiring behavior information of the clinical research practitioner in each business system; constructing a label library based on the base information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels; receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item; responding the partner request to output information of a target clinical research practitioner, wherein a group of labels corresponding to the target clinical research practitioner are matched with the clinical research project, so that the problems of non-uniform management and low management efficiency and accuracy of user information of the clinical research practitioner are solved, the cost of maintaining the user information of the clinical research practitioner by a system is saved, and the clinical research project partner can be more accurate and efficient by constructing a user label library of the clinical research practitioner.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a mobile terminal according to an embodiment of the present application;
FIG. 2 is a flow chart of an information matching method for a clinical research practitioner according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a software architecture of a user representation system according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a tag library construction process according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a user representation data generation flow, according to an embodiment of the present application;
FIG. 6 is a block diagram of an information matching apparatus for a clinical research practitioner according to an embodiment of the present application;
fig. 7 is a hardware structure diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The embodiment provides a mobile terminal. Fig. 1 is a block diagram of a mobile terminal according to an embodiment of the present application. As shown in fig. 1, the mobile terminal includes: a Radio Frequency (RF) circuit 110, a memory 120, an input unit 130, a display unit 140, a sensor 150, an audio circuit 160, a wireless fidelity (WiFi) module 170, a processor 180, and a power supply 190. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each constituent element of the mobile terminal in detail with reference to fig. 1:
the RF circuit 110 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives downlink information of a base station and then processes the received downlink information to the processor 180; in addition, the data for designing uplink is transmitted to the base station. In general, RF circuits include, but are not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 110 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.
The memory 120 may be used to store software programs and modules, and the processor 180 executes various functional applications and data processing of the mobile terminal by operating the software programs and modules stored in the memory 120. The memory 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the mobile terminal, and the like. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The input unit 130 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the input unit 130 may include a touch panel 131 and other input devices 132. The touch panel 131, also referred to as a touch screen, may collect touch operations of a user on or near the touch panel 131 (e.g., operations of the user on or near the touch panel 131 using any suitable object or accessory such as a finger or a stylus pen), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 131 may include two parts, i.e., a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 180, and can receive and execute commands sent by the processor 180. In addition, the touch panel 131 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 130 may include other input devices 132 in addition to the touch panel 131. In particular, other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 140 may be used to display information input by a user or information provided to the user and various menus of the mobile terminal. The Display unit 140 may include a Display panel 141, and optionally, the Display panel 141 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 131 can cover the display panel 141, and when the touch panel 131 detects a touch operation on or near the touch panel 131, the touch operation is transmitted to the processor 180 to determine the type of the touch event, and then the processor 180 provides a corresponding visual output on the display panel 141 according to the type of the touch event. Although the touch panel 131 and the display panel 141 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 131 and the display panel 141 may be integrated to implement the input and output functions of the mobile terminal.
The mobile terminal may also include at least one sensor 150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 141 according to the brightness of ambient light, and a proximity sensor that may turn off the display panel 141 and/or the backlight when the mobile terminal is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration) for recognizing the attitude of the mobile terminal, and related functions (such as pedometer and tapping) for vibration recognition; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile terminal, further description is omitted here.
A speaker 161 and a microphone 162 in the audio circuit 160 may provide an audio interface between the user and the mobile terminal. The audio circuit 160 may transmit the electrical signal converted from the received audio data to the speaker 161, and convert the electrical signal into a sound signal for output by the speaker 161; on the other hand, the microphone 162 converts the collected sound signal into an electric signal, converts the electric signal into audio data after being received by the audio circuit 160, and then outputs the audio data to the processor 180 for processing, and then transmits the audio data to, for example, another mobile terminal via the RF circuit 110, or outputs the audio data to the memory 120 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and the mobile terminal can help a user to send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 170, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 170, it is understood that it does not belong to the essential components of the mobile terminal, and it can be omitted or replaced with other short-range wireless transmission modules, such as Zigbee module or WAPI module, etc., as required within the scope not changing the essence of the invention.
The processor 180 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 120 and calling data stored in the memory 120, thereby performing overall monitoring of the mobile terminal. Alternatively, processor 180 may include one or more processing units; preferably, the processor 180 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 180.
The mobile terminal also includes a power supply 190 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 180 via a power management system that may be configured to manage charging, discharging, and power consumption.
Although not shown, the mobile terminal may further include a camera, a bluetooth module, and the like, which will not be described herein.
In this embodiment, the processor 180 is configured to: acquiring basic information of clinical research practitioners in each business system; acquiring behavior information of the clinical research practitioner in each business system; constructing a label library based on the base information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels; receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item; outputting information of a target clinical research practitioner in response to the partner request, wherein a set of labels corresponding to the target clinical research practitioner matches the clinical research project.
In some of these embodiments, the processor 180 is further configured to: extracting a fact label corresponding to the clinical research practitioner from the basic information, wherein the fact label is used for indicating at least one of the following information of the clinical research practitioner: social attributes, clinical study attributes, project performance, content of interest, use tools, liveness; generating a model label corresponding to the clinical research practitioner according to the fact label and the behavior information, wherein the model label is used for indicating at least one of the following information of the clinical research practitioner: the city where the user is located, the facultative intention of the user, the user score and the project experience distribution; generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label, wherein the predictive label is indicative of at least one of the following information for the clinical research practitioner: demographic attributes, content of interest, recent trends, potential for user participation in clinical research projects; wherein the set of tags includes at least one of the fact tag, the model tag, and the prediction tag.
In some of these embodiments, the processor 180 is further configured to: and taking the fact label and the behavior information as input parameters of a pre-trained model, obtaining output parameters of the pre-trained model, and determining the output parameters as the model labels corresponding to the clinical research practitioners, wherein the pre-trained model is used for indicating the corresponding relationship between the fact label corresponding to the clinical research practitioners and the behavior information of the clinical research practitioners and the model labels corresponding to the clinical research practitioners.
In some of these embodiments, the processor 180 is further configured to: clustering the clinical research practitioners according to the fact labels and the model labels; the predictive label is configured for each type of clinical research practitioner.
In some of these embodiments, the processor 180 is further configured to: after building a library of labels based on the base information and the behavioral information, pushing information to the clinical research practitioner that matches a set of labels corresponding to the clinical research practitioner.
In some of these embodiments, the processor 180 is further configured to: after behavior information of the clinical research practitioner in each business system is obtained, normalization processing is performed on the behavior information to obtain processed behavior information, wherein the processed behavior information comprises: a time stamp indicating a time at which an event was performed by the clinical research practitioner, an event identification identifying an event performed by the clinical research practitioner, and a behavior identification indicating a behavior action made during the performance of an event by the clinical research practitioner; and monitoring the user behavior of the clinical research practitioner according to the processed behavior information.
In some of these embodiments, the processor 180 is further configured to: user behavior of the clinical research practitioner is monitored from at least one of a time dimension, an event dimension, and a behavior dimension.
The present embodiment provides an information matching method for clinical research practitioners. Fig. 2 is a flowchart of an information matching method for clinical research practitioners according to an embodiment of the present application, and as shown in fig. 2, the flowchart includes the following steps:
step S201, acquiring basic information of clinical research practitioners in each business system;
step S202, acquiring behavior information of the clinical research practitioner in each business system;
step S203, constructing a label library based on the basic information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a group of labels;
step S204, receiving a partner request for a clinical study item, wherein the partner request is for requesting a clinical study practitioner to be matched for the clinical study item;
and S205, responding to the partner request, and outputting information of a target clinical research practitioner, wherein a group of labels corresponding to the target clinical research practitioner is matched with the clinical research project.
Through the steps, basic information of clinical research practitioners in each business system is obtained; acquiring behavior information of the clinical research practitioner in each business system; constructing a label library based on the base information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels; receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item; responding the partner request to output information of a target clinical research practitioner, wherein a group of labels corresponding to the target clinical research practitioner are matched with the clinical research project, so that the problems of non-uniform management and low management efficiency and accuracy of user information of the clinical research practitioner are solved, the cost of maintaining the user information of the clinical research practitioner by a system is saved, and the clinical research project partner can be more accurate and efficient by constructing a user label library of the clinical research practitioner.
The embodiment of the application can establish a uniform user center and provide a maintenance and access interface for each service system to use when the user information is changed, so that the uniform management and uniform storage of the user information are achieved. The business system may include, but is not limited to: customer service system, operation system, project management system, etc.
In some embodiments, the user center can uniformly collect and manage the social attributes, clinical research attributes and other basic information of clinical research practitioners across business systems based on user accounts. The user center establishes a uniform account system (a mobile phone number or a mailbox is used as an account of a user, and one user is uniquely marked), provides a uniform account API, and all service systems are uniformly accessed. The user center provides a uniform user information management API, and the service system reports the user information through the user account. The collected basic user information mainly comprises: social attributes (family members, work experience, educational experience, contact details, honor certificates, bank card information, personal skills, etc.) and clinical research attributes (facultative willingness work nature, willingness hospital, willingness area, willingness field, willingness post; user's current occupation, years of work in clinical industry, past clinical research project experience, etc.).
The embodiment of the present application may further establish a user profiling system, which may acquire basic information of clinical research practitioners from a user center, and then perform the above steps S202 to S205.
As shown in FIG. 3, the user representation system can be divided into six layers, i.e., a source layer, an acquisition layer, a storage layer, a service layer, an application layer and a presentation layer, from the software architecture level. The information source layer and the information storage layer mainly realize the acquisition of various basic data; a plurality of types of business models and label algorithms are constructed in the service layer, and data modeling is carried out on various types of basic data completing the cleaning and sorting process; the application layer realizes various user labels and user portraits and various derived services based on the user labels and the user portraits, such as: user classification, content recommendation, behavior analysis, and the like, forming a plurality of independent functional modules or components; the display layer utilizes a data visualization technology to visually present user figures, operation data and the like, and displays the user figures to different service lines in various display modes such as statistical reports, charts and the like.
In order to realize the collection, cleaning and analysis of massive user behavior log data of each channel, a log collection system based on an ELK big data analysis architecture is established for a user portrait system. In the concrete implementation, each service system is embedded (covering diversified channels such as websites, WAP (wireless application protocol), App (application program application) and the like), event parameters are defined, data messages are sent to an acquisition layer (for example, coordinates of a user are sent to the acquisition layer when a mobile terminal user is active) based on an event trigger mechanism and an http request, personalized log acquisition based on different applications is realized, and the personalized log acquisition is finally stored in an elastic search cluster for storage, so that a foundation is laid for subsequent user behavior analysis.
After acquiring basic information and behavior information of clinical research practitioners, a service layer of a user portrait system constructs a business model and a label algorithm, performs data modeling on various types of cleaned basic data, constructs a label library based on the basic information and the behavior information, and realizes user labeling
In some embodiments, as shown in fig. 4, constructing the tag library based on the basic information and the behavior information includes:
extracting a fact label corresponding to the clinical research practitioner from the basic information, wherein the fact label is used for indicating at least one of the following information of the clinical research practitioner: social attributes, clinical study attributes, project performance, content of interest, usage tools, liveness. Wherein, the project execution situation may include, but is not limited to, status information of in-progress, completion, etc.; the content of interest may include, but is not limited to, the field of clinical research, etc.; the usage tool may include, but is not limited to, an application APP or the like; liveness may include, but is not limited to, active cities, etc.
Generating a model label corresponding to the clinical research practitioner according to the fact label and the behavior information, wherein the model label is used for indicating at least one of the following information of the clinical research practitioner: the city of the user, the facultative intention of the user, the user rating and the project experience distribution. Wherein, the user score may include, but is not limited to, project completion, user evaluation, etc.; the project experience distribution may include, but is not limited to, a city of the project distribution, a domain of the project distribution, and the like.
Generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label, wherein the predictive label is indicative of at least one of the following information for the clinical research practitioner: demographic attributes, content of interest, recent trends, potential for user participation in clinical research projects. Wherein the population attributes may include, but are not limited to, tumor research groups, Beijing groups of clinical research practitioners, and the like; recent dynamics may include, but are not limited to, whether the clinical research practitioner is in an idle state, etc.; the potential of a user to participate in a clinical study may include, but is not limited to, whether a clinical research practitioner has the ability to perform the clinical study.
Wherein the set of labels corresponding to the clinical research practitioner in the label library includes at least one of the fact label, the model label, and the predictive label.
In some of these embodiments, generating a model label for the clinical research practitioner from the fact label and the behavioral information comprises:
and taking the fact label and the behavior information as input parameters of a pre-trained model, obtaining output parameters of the pre-trained model, and determining the output parameters as the model labels corresponding to the clinical research practitioners, wherein the pre-trained model is used for indicating the corresponding relationship between the fact label corresponding to the clinical research practitioners and the behavior information of the clinical research practitioners and the model labels corresponding to the clinical research practitioners.
In some of these embodiments, generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label comprises:
clustering the clinical research practitioners according to the fact labels and the model labels;
the predictive label is configured for each type of clinical research practitioner.
As shown in fig. 5, the user profile data acquisition module in the user profile system performs dimension labeling and feature value calculation according to the collected basic information of clinical research practitioners, labels the basic attributes thereof, generates a fact label, and stores the fact label in the user profile system. The user portrait feature processing module synthesizes the fact label, abstracts the behavior information of clinical research practitioners: through dimension calculation and feature generation, model labels such as user activity information (such as a city where the user is located), user part-job intention and the like are generated. The user portrait clustering module performs clustering and classification (for example, classification according to dimensions such as regions, occupation and project experience) on the users through clustering center division and clustering result generation. The user type labeling module predicts the crowd attributes, interesting contents, recent dynamics, the potential of the user for participating in clinical research projects and the like of the user based on the existing fact labels and model notes, and the predicted attributes are prediction labels of clinical research practitioners.
Fact label: given facts, it is extracted from the raw data (including the underlying information and the behavioral information). For example, the user basic attribute is obtained through user setting: age, gender, birthday, native place, current location, year of operation, hospital of interest, etc.
Model labeling: abstraction and clustering of fact labels and behavior information: recent active areas, whether or not there is a part-time interest, an area of interest, an intended job, recent projects being made, recent cities, recent hospitals, user ratings, project experience distributions, and the like.
And (3) predicting a label: based on the fact labels and the model labels, the working experience, working years, good areas, interesting contents, recent trends, the potential of the user to participate in clinical research projects, and the like of the user are predicted.
In the process of constructing the user label, the specific user labels with multiple dimensions including the current occupation, the working years, the cooperative intention, the past work experience, the adept field, the research project experience, the user attention hospital, the recent city of the user and the like are established by combining the service characteristics of the clinical research industry; meanwhile, the service records of the user in the project execution process are combined, the client evaluation records are combined to establish user tags, and the individual or group characteristics of the user are quickly positioned through matching and combination of the user tags.
In some of these embodiments, after the construction of a library of tags for a clinical research practitioner, the tag data can be presented in multiple dimensions for different purposes. The basic user portrait is outlined through various basic charts and user labels, and independent instrument panels and data analysis tools can be designed for different groups such as user operation teams, customer service teams and decision makers.
In some of these embodiments, after constructing a library of labels for clinical research practitioners, the user profiling system may receive a partner request for a clinical research project and select and output information for a targeted clinical research practitioner whose corresponding set of labels matches the clinical research project based on the constructed library of labels.
For example, when a clinical research project requires a project person (CRA/CRC), the corresponding requirements are sent to a clinical human resources manager; matching the intentional users by the human resource manager in a user operation system according to the user part-time willingness information, and recommending the users (resumes) to the project manager after communication confirmation with the users; if the project manager confirms that the recommendation is accepted, the project manager performs subsequent concurrent agreement with the user; after the agreement is successfully signed, the user enters into a clinical research project.
In the design process of a user portrait system, user portraits with different dimensions are used as the main basis of intelligent clustering of users, automatic and intelligent user clustering and grouping are technically realized, operators are helped to quickly match target users and send project managers of clinical research projects, so that a quick communication channel is established, and connected users are helped to quickly enter the clinical research projects to carry out clinical research work.
In some of these embodiments, after a library of labels for a clinical research practitioner is constructed, information matching a set of labels corresponding to the clinical research practitioner may also be pushed to the clinical research practitioner.
The user portrays the system and has built-in multiple nimble swift message content editing tool to the operation channel of difference to realize the seamless connection of user's screening and operation, help the business department to realize the quick message propelling movement of multiple channels such as APP, mail, SMS, and provide the statistics feedback function of propelling movement effect, the operation team of being convenient for carries out real time monitoring to the operation effect.
In some embodiments, after acquiring behavior information of the clinical research practitioner in each business system, the behavior information may further be normalized to obtain processed behavior information, where the processed behavior information includes: a time stamp indicating a time at which an event was performed by the clinical research practitioner, an event identification identifying an event performed by the clinical research practitioner, and a behavior identification indicating a behavior action made during the performance of an event by the clinical research practitioner; the user behavior of the clinical research practitioner is then monitored based on the processed behavior information.
Specifically, monitoring the user behavior of the clinical research practitioner according to the processed behavior information includes: user behavior of the clinical research practitioner is monitored from at least one of a time dimension, an event dimension, and a behavior dimension. The processed behavior information can be subjected to statistical analysis from the three dimensions, so that the user behavior can be comprehensively and accurately monitored and analyzed.
In some embodiments, monitoring the user behavior of the target user from at least one of a time dimension, an event dimension, and a behavior dimension may include at least one of:
obtaining a number of target events and/or target behavioral operations performed by the clinical research practitioner over a target time period;
determining an associative relationship between a plurality of events performed by the clinical research practitioner;
determining an associative relationship between a plurality of behavioral operations performed by the clinical research practitioner.
It should be noted that the target time period may be any time period determined by a timestamp, the target event may be uniquely indicated by a target event identifier, and the target behavior operation may be uniquely indicated by a target behavior operation identifier. The number of the target events can be obtained by counting the number of the target event identifications in the behavior information of the target stamp in the target time period, and the number of the target behavior operations can be obtained by counting the number of the target behavior identifications in the behavior information of the target stamp in the target time period. According to the actual monitoring and analyzing requirements, the number of the target behavior operations can be counted, namely the number of the target behavior operations in one event, and also can be counted, namely the number of the target behavior operations in a plurality of events.
It should be further noted that analyzing the association relationship between the events and the association relationship between the behavior operations can help to restore the user behavior more accurately, and improve the accuracy of detecting and analyzing the user behavior. The incidence relation between a plurality of events and the incidence relation between a plurality of behavior operations can be determined by whether the same behavior operation exists in the events.
In some embodiments, monitoring the user behavior of the target user from at least one of a time dimension, an event dimension, and a behavior dimension comprises:
judging whether the user behaviors of the clinical research practitioner meet user behavior rules in a preset user behavior rule base, wherein the user behavior rules are used for indicating the user behaviors allowed to be executed by the clinical research practitioner;
and when the user behavior of the clinical research practitioner does not accord with the user behavior rule, performing message reminding.
It should be noted that, a plurality of user behavior rules may be configured in advance in the user behavior rule base, and the monitoring analysis may be performed on the user behavior by performing user behavior rule matching on the processed behavior information. The embodiment of the application can also be provided with an early warning rule base and an alarm rule base which are matched with the user behavior rule base in advance, wherein when the user behavior of a clinical research practitioner is determined not to accord with the user behavior rule, early warning can be carried out according to the early warning rule, or alarm can be carried out according to the alarm rule. By means of an early warning mechanism or an alarming mechanism, the use risk of the system can be avoided, and the safety of the system is guaranteed.
In some of these embodiments, where the user behaviour of the clinical research practitioner does not comply with the user behaviour rules, making a message alert may comprise: and multi-terminal message reminding, namely pushing a reminding message to a clinical research practitioner, pushing a reminding message to a business system and pushing a reminding message to a user representation system, wherein the content of the reminding message can be the user behavior obtained through monitoring and analysis and the user behavior rule which is not in accordance with the user behavior. The reminding message is pushed, so that the user behavior with safety risk can be effectively processed quickly, and the purpose of guaranteeing the system safety is achieved.
In some embodiments, after the message is reminded when the user behavior of the target user does not meet the user behavior rule, the embodiment of the present application may also perform behavior limitation on the user behavior that does not meet the user behavior rule.
According to the embodiment of the application, the basic information of clinical research practitioners is uniformly collected and managed by the cross-business system, so that the system maintenance cost can be saved. And data such as user resumes, user training records, project experiences and the like are continuously collected through multiple channels, and the requirements of user culture and data management are met. And analyzing and counting the collected user behavior data to form a user portrait, and outputting portrait searching capability to each service system. By establishing the user tag library, a rapid communication channel is established for operators, the operators are helped to connect users and finally enter clinical research projects to carry out clinical research work, and the clinical research project partners become more efficient. The APP information is recommended in a personalized mode, the information requirements of different types of clinical research practitioners are met, and the clinical research practitioners are helped to find out appropriate project opportunities.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The present embodiment provides an information matching apparatus for clinical research practitioners, which is used to implement the above embodiments and preferred embodiments, and the description of the apparatus is omitted here. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 6 is a block diagram showing the configuration of an information matching apparatus for clinical research practitioners according to an embodiment of the present application, and as shown in fig. 6, the apparatus includes:
a first obtaining unit 61, configured to obtain basic information of clinical research practitioners in each business system;
a second obtaining unit 62, configured to obtain behavior information of the clinical research practitioner in each business system;
a construction unit 63 configured to construct a label library based on the basic information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a group of labels;
a receiving unit 64 for receiving a partner request for a clinical study item, wherein the partner request is for requesting a match of a clinical study practitioner for the clinical study item;
an output unit 65 for outputting information of a target clinical research practitioner in response to the partner request, wherein a set of labels corresponding to the target clinical research practitioner matches the clinical research project.
In some of these embodiments, the building unit 63 comprises:
an extracting module, configured to extract a fact label corresponding to the clinical research practitioner from the basic information, where the fact label is used to indicate at least one of the following information of the clinical research practitioner: social attributes, clinical study attributes, project performance, content of interest, use tools, liveness;
a first generating module, configured to generate a model label corresponding to the clinical research practitioner according to the fact label and the behavior information, where the model label is used to indicate at least one of the following information of the clinical research practitioner: the city where the user is located, the facultative intention of the user, the user score and the project experience distribution;
a second generating module for generating a predictive label corresponding to the clinical research practitioner according to the fact label and the model label, wherein the predictive label is used for indicating at least one of the following information of the clinical research practitioner: demographic attributes, content of interest, recent trends, potential for user participation in clinical research projects;
wherein the set of tags includes at least one of the fact tag, the model tag, and the prediction tag.
In some of these embodiments, the first generating module comprises:
and the determining submodule is used for taking the fact label and the behavior information as input parameters of a pre-trained model to obtain output parameters of the pre-trained model, and determining the output parameters as the model labels corresponding to the clinical research practitioners, wherein the pre-trained model is used for indicating the corresponding relation between the fact label corresponding to the clinical research practitioner and the behavior information of the clinical research practitioner and the model labels corresponding to the clinical research practitioners.
In some of these embodiments, the second generating module comprises:
a clustering sub-module for clustering the clinical research practitioner according to the fact label and the model label;
a configuration sub-module for configuring the predictive label for each type of clinical research practitioner.
In some of these embodiments, the apparatus further comprises:
a pushing unit, configured to push information matching a set of labels corresponding to the clinical research practitioner after constructing a label library based on the basic information and the behavior information.
In some of these embodiments, the apparatus further comprises:
a processing unit, configured to, after acquiring behavior information of the clinical research practitioner in each business system, perform normalization processing on the behavior information to obtain processed behavior information, where the processed behavior information includes: a time stamp indicating a time at which an event was performed by the clinical research practitioner, an event identification identifying an event performed by the clinical research practitioner, and a behavior identification indicating a behavior action made during the performance of an event by the clinical research practitioner;
and the monitoring unit is used for monitoring the user behavior of the clinical research practitioner according to the processed behavior information.
In some of these embodiments, the monitoring unit comprises:
a monitoring module for monitoring user behavior of the clinical research practitioner from at least one of a time dimension, an event dimension, and a behavior dimension.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
An embodiment provides a computer device. The information matching method for clinical research practitioners in combination with the embodiment of the application can be realized by computer equipment. Fig. 7 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may comprise a processor 71 and a memory 72 in which computer program instructions are stored.
Specifically, the processor 71 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 72 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 71.
The processor 71 is configured to read and execute the computer program instructions stored in the memory 72 to implement the information matching method of any one of the clinical research practitioners in the above embodiments.
In some of these embodiments, the computer device may also include a communication interface 73 and a bus 70. As shown in fig. 7, the processor 71, the memory 72, and the communication interface 73 are connected via the bus 70 to complete mutual communication.
The communication interface 73 is used for realizing communication among modules, devices, units and/or equipment in the embodiment of the present application. The communication interface 73 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 70 comprises hardware, software, or both that couple the components of the computer device to one another. Bus 70 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 70 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 70 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
In addition, in combination with the information matching method of clinical research practitioners in the above embodiments, the embodiments of the present application may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions when executed by a processor implement the information matching method of any one of the clinical research practitioners described in the embodiments above.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An information matching method for a clinical research practitioner, comprising:
acquiring basic information of clinical research practitioners in each business system;
acquiring behavior information of the clinical research practitioner in each business system;
constructing a label library based on the base information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels;
receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item;
outputting information of a target clinical research practitioner in response to the partner request, wherein a set of labels corresponding to the target clinical research practitioner matches the clinical research project.
2. The method of claim 1, wherein building a tag library based on the base information and the behavior information comprises:
extracting a fact label corresponding to the clinical research practitioner from the basic information, wherein the fact label is used for indicating at least one of the following information of the clinical research practitioner: social attributes, clinical study attributes, project performance, content of interest, use tools, liveness;
generating a model label corresponding to the clinical research practitioner according to the fact label and the behavior information, wherein the model label is used for indicating at least one of the following information of the clinical research practitioner: the city where the user is located, the facultative intention of the user, the user score and the project experience distribution;
generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label, wherein the predictive label is indicative of at least one of the following information for the clinical research practitioner: demographic attributes, content of interest, recent trends, potential for user participation in clinical research projects;
wherein the set of tags includes at least one of the fact tag, the model tag, and the prediction tag.
3. The method of claim 2, wherein generating a model label corresponding to the clinical research practitioner from the fact label and the behavioral information comprises:
and taking the fact label and the behavior information as input parameters of a pre-trained model, obtaining output parameters of the pre-trained model, and determining the output parameters as the model labels corresponding to the clinical research practitioners, wherein the pre-trained model is used for indicating the corresponding relationship between the fact label corresponding to the clinical research practitioners and the behavior information of the clinical research practitioners and the model labels corresponding to the clinical research practitioners.
4. The method of claim 2, wherein generating a predictive label corresponding to the clinical research practitioner from the fact label and the model label comprises:
clustering the clinical research practitioners according to the fact labels and the model labels;
the predictive label is configured for each type of clinical research practitioner.
5. The method of claim 1, wherein after building a tag library based on the base information and the behavior information, the method further comprises:
pushing information to the clinical research practitioner that matches a set of labels corresponding to the clinical research practitioner.
6. The method of claim 1, wherein after obtaining information on the behaviour of the clinical research practitioner in the respective business system, the method further comprises:
normalizing the behavior information to obtain processed behavior information, wherein the processed behavior information comprises: a time stamp indicating a time at which an event was performed by the clinical research practitioner, an event identification identifying an event performed by the clinical research practitioner, and a behavior identification indicating a behavior action made during the performance of an event by the clinical research practitioner;
and monitoring the user behavior of the clinical research practitioner according to the processed behavior information.
7. The method of claim 6, wherein monitoring user behavior of the clinical research practitioner from the processed behavior information comprises:
user behavior of the clinical research practitioner is monitored from at least one of a time dimension, an event dimension, and a behavior dimension.
8. An information matching apparatus for a clinical research practitioner, comprising:
the first acquisition unit is used for acquiring basic information of clinical research practitioners in each business system;
the second acquisition unit is used for acquiring behavior information of the clinical research practitioner in each business system;
a construction unit configured to construct a label library based on the basic information and the behavior information, wherein each clinical research practitioner in the label library corresponds to a set of labels;
a receiving unit for receiving a partner request for a clinical study item, wherein the partner request is for a request to match a clinical study practitioner for the clinical study item;
and the output unit is used for responding to the partner request and outputting the information of the target clinical research practitioner, wherein a group of labels corresponding to the target clinical research practitioner is matched with the clinical research project.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the information matching method for a clinical research practitioner of any one of claims 1 to 7.
10. A computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the information matching method for a clinical research practitioner according to any one of claims 1 to 7.
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